Tag: Healthcare Analytics

When compared to the HIMSS conference that attracted 38,000 people from the healthcare industry, the Health Datapalooza, an event that was organized in Washington, D.C. this week, was relatively low key – about 2,000 people attended it, including folks from the government (one of its organizers). Here’s Todd Park, CTO of the US government introducing the event to entrepreneurs and others.

But interest in similar events could explode as creative companies create meaningful information out of the increasing number of datasets that the government is making public. In a New York Times op-ed, Tom Friedman contemplated a healthcare Silicon Valley that could become a platform for innovation based on health data.

This week, NPR covered Health Datapalooza that is becoming a showcase event to launch apps based on public health data. The story talked about startups that are doing something interesting with Medicare’s recently released datasets: Lyfechannel creates apps that help senior citizens talk to doctors about medical care and Accordion Healthhelps families estimate health expenses. Even a post-graduate fellowship in health data science was launched at the event.

A Public Tool That Reveals Your Doctor’s Intentions

However, the most interesting data visualization from Medicare’s datasets is from ProPublica, a nonprofit that conducts investigative journalism. The tool is called Treatment Tracker.

Being in the revenue cycle business, I was curious to see how the data compared across individual doctors and groups. For any of the 880,000 physicians who submitted claims to Medicare in 2012, the tool shows information on billing, coding and data about their treatment protocols (where did patients go before and after treatment). Spurred by the incentive program, more than 380,000 eligible professionals (mostly doctors) have submitted EHR information in the last three years. Though there’s no wind of it yet, it’s only a matter of time when de-identified health records become public.

This brings a fundamentally different level of transparency to physician services, their behavior and payments. It will compel large EHR vendors who’ve built business models based on hoarding data to become more open. Everything will be out in the sun.

Four Sample Datasets:

RockHealth reported 10 dataset sources that can be used to build a variety of tools and apps. Here are the most interesting ones.

3. openFDA includes reports on drug adverse events, such as adverse reactions or medication errors submitted.

Global Implications

These are early examples of government initiatives to actively make data available in standardized formats for innovation (read about the US DATA act). A search on UK’s OpenData reveals 652 open National Health Service datasets covering statistics from obesity to alcohol consumption. India’s Open Data Initiative has 7,700+ datasetsthat include healthcare datasets covering immunization and disease statistics on AIDS. Over time, most progressive governments will follow the US path and steadily reveal population health data hoping for innovative fixes to healthcare problems.

Wisdom Wanted

There’s a lot of data out there but very little by way of wisdom that can impact how healthcare decisions are made globally. That’s where healthcare entrepreneurs, data analysts and AI algorithms come in. A newer way to understand and present information can bring greater transparency and efficiency, curtailing healthcare fraud and bringing clarity to patient care.

Statistics show that over 50 percent of all medical facilities have successfully transitioned towards implementing an electronic health record system. While implementing EHR may mean streamlining operations and going paperless, the process tends to become mechanical and many look at it as mere data entry over time.

Practices are unaware of the enormous amount of data they produce every day. Capturing vitals, physical exams, systems reviews and checking/prescribing medications are all forms of generating data. Typically, an EHR is an archive of data which, if used to its potential, can lead to interesting insights.

Here are five insights you can acquire with your generated data:

1. Population breakdown: The analysis of patient type that forms your patient pool. It is the means for distinguishing your patients as per gender and age. Mapping your patients against their corresponding BMI values helps to track how healthy (or unhealthy) your patients are.
2. Diagnosis chart: The top 10 diagnosis among patient population. This will help determine the most and the least occurring conditions.
3. Condition number: The number for patients who have less than or more than two medical conditions.
4. Drug report: A report of the top drugs prescribed by you and the drugs that required the most substitution.
5. Risk profiles: Maintaining risk profiles of your patient population and sorting patients based on low, medium and high risk profiles. The patients falling in the high risk pool may need a more personalized medical approach.

Healthcare organizations are pushing towards risk-sharing payment models where reimbursements are tied to quality of care instead to quantity. By adopting EHRs, we may just be scratching the surface of something bigger in the years to come. Technology will continue playing a greater role and having analytical insights will empower precise medical judgments.